Issue No. 03 - March (2012 vol. 24)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2010.215
Lei Wu , University of Science and Technology of China, China
Steven C.H. Hoi , Nanyang Technological University, Singapore
Rong Jin , Michigan State University, East Lansing
Jianke Zhu , Chinese University of Hong Kong, Hong Kong
Nenghai Yu , University of Science and Technology of China, China
Learning distance functions with side information plays a key role in many data mining applications. Conventional distance metric learning approaches often assume that the target distance function is represented in some form of Mahalanobis distance. These approaches usually work well when data are in low dimensionality, but often become computationally expensive or even infeasible when handling high-dimensional data. In this paper, we propose a novel scheme of learning nonlinear distance functions with side information. It aims to learn a Bregman distance function using a nonparametric approach that is similar to Support Vector Machines. We emphasize that the proposed scheme is more general than the conventional approach for distance metric learning, and is able to handle high-dimensional data efficiently. We verify the efficacy of the proposed distance learning method with extensive experiments on semi-supervised clustering. The comparison with state-of-the-art approaches for learning distance functions with side information reveals clear advantages of the proposed technique.
Bregman distance, distance functions, metric learning, convex functions.
L. Wu, S. C. Hoi, R. Jin, N. Yu and J. Zhu, "Learning Bregman Distance Functions for Semi-Supervised Clustering," in IEEE Transactions on Knowledge & Data Engineering, vol. 24, no. , pp. 478-491, 2010.